Research on the dynamics of trade performance in Vojvodina

Abstract

The trade performance of a particular region is rather challenging to research, especially when multicriteria decision-making methods are applied, in addition to the classic methodology - ratio analysis. This paper analyses trade performance in Vojvodina based on the LMAW (Logarithm Methodology of Additive Weights) and DNMA (Double Normalization-based Multiple Aggregation) methods. The research aims to assess trade performance in Vojvodina as realistically as possible and improve it in the future by applying relevant measures. According to the results of the DNMA method, the top five years in terms of trade performance in Vojvodina include 2022, 2021, 2020, 2019 and 2016. Therefore, the best trade performance in Vojvodina was in 2022 and the worst in 2011. Recently, a slight increase in trade performance has been noticeable in Vojvodina. In order to achieve the target performance of trade in Vojvodina, labor productivity should, among other things, be significantly increased. In that direction, employees should be stimulated with training, rewards, career advancement, flexible employment, health and pension insurance, etc. Likewise, the relationship between employees and consumers should be improved to increase turnover and achieve the target profit. No similar studies have used other multicriteria decision-making methods. This fact limits the possibilities of comparing the results obtained using different multicriteria decision-making methods. For these reasons, it is recommended that trade performance in Vojvodina be analysed by applying different multicriteria decision-making methods. This paper?s scientific and professional contribution is reflected in the fact that, as far as it is known, for the first time, the dynamics of trade performance in Vojvodina are analysed using modern multicriteria decision-making methods. Concerning the ratio analysis, the obtained results are more realistic as a basis for improving trade performance in Vojvodina in the future by applying relevant measures.